AI-Assisted Comprehensive Management for Cancer Patients With Comorbidities (GCOG-CG001)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-assisted comprehensive management system.
- Who it may be relevant to
- Registry conditions: Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition). Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
The Impact of Multimodal Digital Fusion AI-Assisted Decision Support System-Based Comprehensive Management on Clinical Outcomes in County-Level Patients With Comorbid Cancer:A Prospective Non-randomized Controlled Interventional Study.
Overview
Combined with the digital whole process management data pool, a multi-modal data fusion framework is developed, and an AI model is established to realize risk stratification and personalized treatment Recommendation and dynamic prognosis prediction; validation of whole-process management based on multimodal digital fusion AI-aided decision support system through prospective non-randomized controlled interventional study The effect on survival, complication control and utilization of medical resources in patients with comorbid malignant tumors.
Detailed description
The title of this study is"The Impact of Multimodal Digital Fusion AI-Assisted Decision Support System-Based Comprehensive Management on Clinical Outcomes in County-Level Patients with Comorbid Cancer: A prospective non-randomized controlled interventional study", to evaluate the impact of full-course management based on a multimodal digital fusion AI-assisted decision support system on the clinical outcomes of county-level oncologic comorbid patients through a prospective non-randomized controlled interventional study. The study plans to enroll 5,000 patients with pathologically confirmed malignancies and at least one comorbid condition (diabetes, hypertension, etc.) , in the first stage, the epidemiological characteristics of co-morbidity and its impact on prognosis, treatment response and quality of life were analyzed In the second phase, patients with comorbid pulmonary malignancies were selected to compare the clinical effects of the voluntary whole-process management group (including personalized intervention such as nutritional screening and dynamic monitoring) and the conventional treatment group, the third stage integrates multi-center Electronic Medical Records, genomic data, wearable device monitoring and other multi-modal data to construct an AI decision-making system, developing risk stratification, personalized treatment recommendation, and dynamic prognostic prediction models, finally, the differences in core indicators such as survival rate (PFS, OS) , complication control and medical resource efficiency between AI-assisted management and traditional mode were compared. This study realizes the integrated intervention of in-hospital and out-of-hospital through digital whole-process management, which is expected to provide an AI-driven precise decision support paradigm for primary medical institutions and improve the efficiency of comprehensive management of tumor comorbidity.
Interventions
- Other AI-assisted comprehensive management system
Precision Risk Stratification and personalized treatment recommendation through AI models may improve the suitability of treatment regimens and thus reduce the incidence of antineoplastic therapy-related adverse effects (e.g. , reduction of chemotherapy toxicity through nutritional intervention) , and improve the efficacy of chemotherapy, and prolonged progression-free survival (PFS) and overall survival (OS)
Primary outcome measures
- Progression-free survival (PFS) [Time frame: 24 months]
- Overall survival (OS） [Time frame: 24 months]
Secondary outcome measures (4)
- Comorbidity control rate. [Time frame: 24 months]
- Quality of life(QLQ-C30). [Time frame: 24 months]
- Medical resource consumption index. [Time frame: 24 months]
- Adherence to AI system interventions. [Time frame: 24 months]
Eligibility criteria
Inclusion criteria
- Patients with a definite diagnosis of malignancy by histopathology and/or cytology;
- Age ≥18 years;
- There is no gender limit
- Plan to receive antineoplastic therapy within 2 weeks or are receiving standard antineoplastic care (surgery, radiation, chemotherapy, or targeted therapy) ;
- Conscious and able to answer questions and use electronic devices autonomously;
- Patients were able to understand the study and voluntarily sign an informed consent form;
Exclusion criteria
- Having severe mental or cognitive impairments that prevent them from understanding the content of the study or implementing the programme;
- With severe heart disease, acute respiratory failure, liver kidney failure and other critical illness;
- Women during pregnancy or lactation；
- Have participated in other interventional studies in the past 1 month or are currently participating;
- Patients with ECOG ≥ 3 that do not respond to treatment;
- Patients with an expected survival of < 3 months that do not respond to treatment;
- Cases deemed unsuitable for enrollment by the investigator.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Non-randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Treatment
Study locations
China · 1 center
- The First Affiliated Hospital of Xinxiang Medical University — Xinxiang
Publications
- Stairmand J, Signal L, Sarfati D, Jackson C, Batten L, Holdaway M, Cunningham C. Consideration of comorbidity in treatment decision making in multidisciplinary cancer team meetings: a systematic review. Ann Oncol. 2015 Jul;26(7):1325-32. doi: 10.1093/annonc/mdv025. Epub 2015 Jan 20. PMID 25605751
- Ding R, Zhu D, He P, Ma Y, Chen Z, Shi X. Comorbidity in lung cancer patients and its association with medical service cost and treatment choice in China. BMC Cancer. 2020 Mar 24;20(1):250. doi: 10.1186/s12885-020-06759-8. PMID 32209058
- Chao C, Page JH, Yang SJ, Rodriguez R, Huynh J, Chia VM. History of chronic comorbidity and risk of chemotherapy-induced febrile neutropenia in cancer patients not receiving G-CSF prophylaxis. Ann Oncol. 2014 Sep;25(9):1821-1829. doi: 10.1093/annonc/mdu203. Epub 2014 Jun 10. PMID 24915871
- Sogaard M, Thomsen RW, Bossen KS, Sorensen HT, Norgaard M. The impact of comorbidity on cancer survival: a review. Clin Epidemiol. 2013 Nov 1;5(Suppl 1):3-29. doi: 10.2147/CLEP.S47150. PMID 24227920
- Jorgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer. 2012 Mar 27;106(7):1353-60. doi: 10.1038/bjc.2012.46. Epub 2012 Feb 21. PMID 22353805
- Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin. 2016 Jul;66(4):337-50. doi: 10.3322/caac.21342. Epub 2016 Feb 17. PMID 26891458
- Wedding U, Roehrig B, Klippstein A, Steiner P, Schaeffer T, Pientka L, Hoffken K. Comorbidity in patients with cancer: prevalence and severity measured by cumulative illness rating scale. Crit Rev Oncol Hematol. 2007 Mar;61(3):269-76. doi: 10.1016/j.critrevonc.2006.11.001. Epub 2007 Jan 4. PMID 17207632
- Abravan A, Faivre-Finn C, Gomes F, van Herk M, Price G. Comorbidity in patients with cancer treated at The Christie. Br J Cancer. 2024 Nov;131(8):1279-1289. doi: 10.1038/s41416-024-02838-w. Epub 2024 Sep 4. PMID 39232185
Identifiers
NCT: NCT07136727 · GCOG-CG001